On the automatic analysis of stellar spectra
This project investigates the problem of automatically extracting and analysing astronomical spectra from large data sets. The . .. three core problems of spectral classification, physical parameterisation, and searching are examined, and a generalisable set of too established based on the technique...
Main Author: | |
---|---|
Published: |
Queen's University Belfast
2008
|
Subjects: | |
Online Access: | http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487587 |
id |
ndltd-bl.uk-oai-ethos.bl.uk-487587 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-bl.uk-oai-ethos.bl.uk-4875872015-03-20T04:54:10ZOn the automatic analysis of stellar spectraWinter, C.2008This project investigates the problem of automatically extracting and analysing astronomical spectra from large data sets. The . .. three core problems of spectral classification, physical parameterisation, and searching are examined, and a generalisable set of too established based on the techniques of artificial neural networks (ANNs), X2 minimisation, and principal components analysis (PCA). These tools are then applied to the archives of the Sloan Digital Sky Survey (SDSS) to automatically extract and analyse the spectra of hot subdwarf stars. Spectral classification is tackled by the versatile statisticalmachine learning method of ANNs. An ANN is trained to classify hot subdwarf spectra onto the classification system defined by Drilling (2006), obtaining global errors (arms) of -2 subtypes for spectral type, -1 subclass for luminosity class,' a'nd -4 subclasses for the helium class. These errors are in line with accuracies achieved by human classifiers. Physical parameters are obtained by fitting observations to grids of theoretical models using a X2 minimisation procedure. A new methodology has been developed for managing and indexing large grids of theoretical models in the '1.2 minimisatio.n code, SFIT. Concepts from the field of computational geometry are used to remove several limitations from this code, and pave the way for· its use in a distributed parallel computing environment. Searching for the spectra of a particular type of object in large, unknown data sets is accomplished using the multivariate statistical technique, PCA. The mechanics of this tool are outlined, and its use demonstrated by searching for hot subdwarf spectra in the SDSS. This solution provides a means to reduce unknown data sets to quantities suitable for visual inspection. 282 spectra of hot subdwarf candidates are obtained from the SDSS and analysed. The results evidence several unexplained p~enomena of extended horizontal branch stars, namely; 1) the existence of the second horizontal branch gap of Newell (1973); 2) two sdB nne -Teff sequences; 3) a high-Teff, log g == 5.0 sequence; and 4) a clustering of hot, helium rich stars along this sequence. These findings pose important questions for stellar evolution theory in the realms of the extended horizontal branch.520Queen's University Belfasthttp://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487587Electronic Thesis or Dissertation |
collection |
NDLTD |
sources |
NDLTD |
topic |
520 |
spellingShingle |
520 Winter, C. On the automatic analysis of stellar spectra |
description |
This project investigates the problem of automatically extracting and analysing astronomical spectra from large data sets. The . .. three core problems of spectral classification, physical parameterisation, and searching are examined, and a generalisable set of too established based on the techniques of artificial neural networks (ANNs), X2 minimisation, and principal components analysis (PCA). These tools are then applied to the archives of the Sloan Digital Sky Survey (SDSS) to automatically extract and analyse the spectra of hot subdwarf stars. Spectral classification is tackled by the versatile statisticalmachine learning method of ANNs. An ANN is trained to classify hot subdwarf spectra onto the classification system defined by Drilling (2006), obtaining global errors (arms) of -2 subtypes for spectral type, -1 subclass for luminosity class,' a'nd -4 subclasses for the helium class. These errors are in line with accuracies achieved by human classifiers. Physical parameters are obtained by fitting observations to grids of theoretical models using a X2 minimisation procedure. A new methodology has been developed for managing and indexing large grids of theoretical models in the '1.2 minimisatio.n code, SFIT. Concepts from the field of computational geometry are used to remove several limitations from this code, and pave the way for· its use in a distributed parallel computing environment. Searching for the spectra of a particular type of object in large, unknown data sets is accomplished using the multivariate statistical technique, PCA. The mechanics of this tool are outlined, and its use demonstrated by searching for hot subdwarf spectra in the SDSS. This solution provides a means to reduce unknown data sets to quantities suitable for visual inspection. 282 spectra of hot subdwarf candidates are obtained from the SDSS and analysed. The results evidence several unexplained p~enomena of extended horizontal branch stars, namely; 1) the existence of the second horizontal branch gap of Newell (1973); 2) two sdB nne -Teff sequences; 3) a high-Teff, log g == 5.0 sequence; and 4) a clustering of hot, helium rich stars along this sequence. These findings pose important questions for stellar evolution theory in the realms of the extended horizontal branch. |
author |
Winter, C. |
author_facet |
Winter, C. |
author_sort |
Winter, C. |
title |
On the automatic analysis of stellar spectra |
title_short |
On the automatic analysis of stellar spectra |
title_full |
On the automatic analysis of stellar spectra |
title_fullStr |
On the automatic analysis of stellar spectra |
title_full_unstemmed |
On the automatic analysis of stellar spectra |
title_sort |
on the automatic analysis of stellar spectra |
publisher |
Queen's University Belfast |
publishDate |
2008 |
url |
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487587 |
work_keys_str_mv |
AT winterc ontheautomaticanalysisofstellarspectra |
_version_ |
1716787067042660352 |